17 KiB
17 KiB
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Line data Source code 1 : // *************************************************************** 2 : // SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez 3 : // SPDX-FileType: SOURCE 4 : // SPDX-License-Identifier: MIT 5 : // *************************************************************** 6 : 7 : #include "KDB.h" 8 : 9 : namespace bayesnet { 10 37 : KDB::KDB(int k, float theta) : Classifier(Network()), k(k), theta(theta) 11 : { 12 111 : validHyperparameters = { "k", "theta" }; 13 : 14 111 : } 15 3 : void KDB::setHyperparameters(const nlohmann::json& hyperparameters_) 16 : { 17 3 : auto hyperparameters = hyperparameters_; 18 3 : if (hyperparameters.contains("k")) { 19 1 : k = hyperparameters["k"]; 20 1 : hyperparameters.erase("k"); 21 : } 22 3 : if (hyperparameters.contains("theta")) { 23 1 : theta = hyperparameters["theta"]; 24 1 : hyperparameters.erase("theta"); 25 : } 26 3 : Classifier::setHyperparameters(hyperparameters); 27 3 : } 28 13 : void KDB::buildModel(const torch::Tensor& weights) 29 : { 30 : /* 31 : 1. For each feature Xi, compute mutual information, I(X;C), 32 : where C is the class. 33 : 2. Compute class conditional mutual information I(Xi;XjIC), f or each 34 : pair of features Xi and Xj, where i#j. 35 : 3. Let the used variable list, S, be empty. 36 : 4. Let the DAG network being constructed, BN, begin with a single 37 : class node, C. 38 : 5. Repeat until S includes all domain features 39 : 5.1. Select feature Xmax which is not in S and has the largest value 40 : I(Xmax;C). 41 : 5.2. Add a node to BN representing Xmax. 42 : 5.3. Add an arc from C to Xmax in BN. 43 : 5.4. Add m = min(lSl,/c) arcs from m distinct features Xj in S with 44 : the highest value for I(Xmax;X,jC). 45 : 5.5. Add Xmax to S. 46 : Compute the conditional probabilility infered by the structure of BN by 47 : using counts from DB, and output BN. 48 : */ 49 : // 1. For each feature Xi, compute mutual information, I(X;C), 50 : // where C is the class. 51 13 : addNodes(); 52 39 : const torch::Tensor& y = dataset.index({ -1, "..." }); 53 13 : std::vector<double> mi; 54 99 : for (auto i = 0; i < features.size(); i++) { 55 258 : torch::Tensor firstFeature = dataset.index({ i, "..." }); 56 86 : mi.push_back(metrics.mutualInformation(firstFeature, y, weights)); 57 86 : } 58 : // 2. Compute class conditional mutual information I(Xi;XjIC), f or each 59 13 : auto conditionalEdgeWeights = metrics.conditionalEdge(weights); 60 : // 3. Let the used variable list, S, be empty. 61 13 : std::vector<int> S; 62 : // 4. Let the DAG network being constructed, BN, begin with a single 63 : // class node, C. 64 : // 5. Repeat until S includes all domain features 65 : // 5.1. Select feature Xmax which is not in S and has the largest value 66 : // I(Xmax;C). 67 13 : auto order = argsort(mi); 68 99 : for (auto idx : order) { 69 : // 5.2. Add a node to BN representing Xmax. 70 : // 5.3. Add an arc from C to Xmax in BN. 71 86 : model.addEdge(className, features[idx]); 72 : // 5.4. Add m = min(lSl,/c) arcs from m distinct features Xj in S with 73 : // the highest value for I(Xmax;X,jC). 74 86 : add_m_edges(idx, S, conditionalEdgeWeights); 75 : // 5.5. Add Xmax to S. 76 86 : S.push_back(idx); 77 : } 78 112 : } 79 86 : void KDB::add_m_edges(int idx, std::vector<int>& S, torch::Tensor& weights) 80 : { 81 86 : auto n_edges = std::min(k, static_cast<int>(S.size())); 82 86 : auto cond_w = clone(weights); 83 86 : bool exit_cond = k == 0; 84 86 : int num = 0; 85 251 : while (!exit_cond) { 86 660 : auto max_minfo = argmax(cond_w.index({ idx, "..." })).item<int>(); 87 165 : auto belongs = find(S.begin(), S.end(), max_minfo) != S.end(); 88 441 : if (belongs && cond_w.index({ idx, max_minfo }).item<float>() > theta) { 89 : try { 90 80 : model.addEdge(features[max_minfo], features[idx]); 91 80 : num++; 92 : } 93 0 : catch (const std::invalid_argument& e) { 94 : // Loops are not allowed 95 0 : } 96 : } 97 660 : cond_w.index_put_({ idx, max_minfo }, -1); 98 495 : auto candidates_mask = cond_w.index({ idx, "..." }).gt(theta); 99 165 : auto candidates = candidates_mask.nonzero(); 100 165 : exit_cond = num == n_edges || candidates.size(0) == 0; 101 165 : } 102 673 : } 103 2 : std::vector<std::string> KDB::graph(const std::string& title) const 104 : { 105 2 : std::string header{ title }; 106 2 : if (title == "KDB") { 107 2 : header += " (k=" + std::to_string(k) + ", theta=" + std::to_string(theta) + ")"; 108 : } 109 4 : return model.graph(header); 110 2 : } 111 : } |
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